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xgmem MCP Memory Server

@meetdhanani17

Repository

Basic information

Category

Memory & Knowledge

Transports

stdio

Author

meetdhanani17

About xgmem MCP Memory Server

Global MCP server that manage all the project data,

#typescript#ai#mcp#vscode#cursor#windsurf#mcp-server

xgmem MCP Memory Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "xgmem": {
      "command": "npx",
      "args": [
        "ts-node",
        "index.ts"
      ]
    }
  }
}

xgmem MCP Memory Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

Fetch the live tool list by running this server in a temporary sandbox using the button above.

Overview

What is xgmem MCP Memory Server?

xgmem is a TypeScript-based Model Context Protocol (MCP) server for project-specific and knowledge graph-based memory. It allows Claude, LLM agents, and other tools to store, retrieve, and manage entities, relations, and observations per project, with support for cross-project knowledge sharing and disk persistence.

How to use xgmem MCP Memory Server?

Add xgmem to your MCP client config (e.g., for Windsurf) using npx -y xgmem@latest. Install dependencies (npm install), build (npm run build), and run in development (npx ts-node index.ts) or production (npm start). For Docker, build the image and mount a volume to persist memory files. To save memories, call the save_project_observations tool via the MCP API with a project ID and observations.

Key features of xgmem MCP Memory Server

  • Knowledge graph storage for entities, relations, and observations
  • Full CRUD operations via MCP tools
  • Disk persistence using a memory.json file per project
  • Docker and TypeScript support for flexible deployment
  • Configurable memory directory via MEMORY_DIR_PATH environment variable

Use cases of xgmem MCP Memory Server

  • Agents and LLMs storing structured memory (entities, relations, observations) per project
  • Cross-project knowledge sharing and migration between different agent sessions
  • Scalable, disk-persistent, and queryable memory for agent ecosystems
  • Saving and retrieving project-specific observations for context-aware AI interactions

Frequently asked questions

1

How does xgmem persist memory?

xgmem saves memory to disk in a `memory.json` file. The storage directory can be changed by setting the `MEMORY_DIR_PATH` environment variable (default: `/app/memories`).

2

What tools does xgmem expose via MCP?

xgmem provides tools including `save_project_observations`, `get_project_observations`, `add_graph_observations`, `create_entities`, `create_relations`, `delete_entities`, `delete_observations`, `delete_relations`, `read_graph`, `search_nodes`, `search_all_projects`, `open_nodes`, and `copy_memory`.

3

How can I run xgmem with Docker?

Build the image with `docker build -t xgmem-mcp-server .` and run with a volume mount: `docker run -v $(pwd)/memories:/app/memories xgmem-mcp-server`. This persists all project memory files on your host.

4

What runtime does xgmem require?

xgmem requires Node.js and npm. It can be run directly on the command line via `npx` or after installing dependencies locally. It also works in Docker without requiring host Node.js installation.

5

Can xgmem share knowledge across projects?

Yes. xgmem is designed for cross-project knowledge sharing and migration. It stores memory per project but allows searching across all projects using the `search_all_projects` tool.

Comments

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